Accelerator diagnosis and control by Neural Nets

Conference · Sun Jan 01 00:00:00 EST 1989

OSTI ID:6216515

Spencer, J E

Neural Nets (NN) have been described as a solution looking for a problem. In the last conference, Artificial Intelligence (AI) was considered in the accelerator context. While good for local surveillance and control, its use for large complex systems (LCS) was much more restricted. By contrast, NN provide a good metaphor for LCS. It can be argued that they are logically equivalent to multi-loop feedback/forward control of faulty systems, and therefore provide an ideal adaptive control system. Thus, where AI may be good for maintaining a 'golden orbit,' NN should be good for obtaining it via a quantitative approach to 'look and adjust' methods like operator tweaking which use pattern recognition to deal with hardware and software limitations, inaccuracies or errors as well as imprecise knowledge or understanding of effects like annealing and hysteresis. Further, insights from NN allow one to define feasibility conditions for LCS in terms of design constraints and tolerances. Hardware and software implications are discussed and several LCS of current interest are compared and contrasted. 15 refs., 5 figs.

• Research Organization: Stanford Linear Accelerator Center, Menlo Park, CA (USA)
• DOE Contract Number: AC03-76SF00515
• OSTI ID: 6216515
• Report Number(s): SLAC-PUB-4849; CONF-890335-165; ON: DE89012789
• Resource Relation: Conference: 13. particle accelerator conference, Chicago, IL, USA, 20 Mar 1989; Other Information: Portions of this document are illegible in microfiche products
• Country of Publication: United States
• Language: English

Related Subjects

43 PARTICLE ACCELERATORS
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
ACCELERATORS
COMPUTERIZED CONTROL SYSTEMS
ARTIFICIAL INTELLIGENCE
DIAGNOSTIC TECHNIQUES
FEEDBACK
RELIABILITY
CONTROL SYSTEMS
430100* - Particle Accelerators- Design
Development
& Operation
990210 - Supercomputers- (1987-1989)